The invention generally relates to automated, robotic and other processing systems, and relates in particular to automated and robotic systems intended for use in environments requiring, for example, that a variety of objects (e.g., articles, parcels or packages) be processed, e.g., sorted and/or otherwise distributed to several output destinations.
Many object distribution systems receive objects in a disorganized stream that may be provided as individual objects or objects aggregated in groups such as in bags, arriving on any of several different conveyances, commonly a conveyor, a truck, a pallet, a Gaylord, or a bin. Each object must then be distributed to the correct destination container, as determined by identification information associated with the object, which is commonly determined by a label printed on the object. The destination container may take many forms, such as a bag or a bin.
The processing of such objects has traditionally been done, at least in part, by human workers that scan the objects, e.g., with a hand-held barcode scanner, and then place the objects at assigned locations. For example many order fulfillment operations achieve high efficiency by employing a process called wave picking. In wave picking, orders are picked from warehouse shelves and placed at locations (e.g., into bins) containing multiple orders that are sorted downstream. At the processing stage individual objects are identified, and multi-object orders are consolidated, for example into a single bin or shelf location, so that they may be packed and then shipped to customers. The processing (e.g., sorting) of these objects has traditionally been done by hand. A human sorter picks an object from an incoming bin, finds a barcode on the object, scans the barcode with a handheld barcode scanner, determines from the scanned barcode the appropriate bin or shelf location for the article, and then places the article in the so-determined bin or shelf location where all objects for that order have been defined to belong. Automated systems for order fulfillment have also been proposed. See for example, U.S. Patent Application Publication No. 2014/0244026, which discloses the use of a robotic arm together with an arcuate structure that is movable to within reach of the robotic arm.
Other ways of identifying objects by code scanning either require manual processing, or require that the code location be controlled or constrained so that a fixed or robot-held code scanner (e.g., barcode scanner) can reliably detect it. Manually operated barcode scanners are generally either fixed or handheld systems. With fixed systems, such as those used at point-of-sale systems, the operator holds the object and places it in front of the scanner so that the barcode faces the scanning device's sensors, and the scanner, which scans continuously, decodes any barcodes that it can detect. If the object is not immediately detected, the person holding the object typically needs to vary the position or rotation of the object in front of the fixed scanner, so as to make the barcode more visible to the scanner. For handheld systems, the person operating the scanner looks for the barcode on the object, and then holds the scanner so that the object's barcode is visible to the scanner, and then presses a button on the handheld scanner to initiate a scan of the barcode.
Further, many current distribution center sorting systems generally assume an inflexible sequence of operations whereby a disorganized stream of input objects is first singulated into a single stream of isolated objects presented one at a time to a scanner that identifies the object. A conveyance element or elements (e.g., a conveyor, a tilt tray, or manually movable bins) transport the objects to the desired destination or further processing station, which may be a bin, a chute, a bag or a conveyor etc.
In conventional parcel sortation systems, human workers or automated systems typically retrieve objects in an arrival order, and sort each object into a collection bin based on a set of given heuristics. For instance, all objects of like type might go to a collection bin, or all objects in a single customer order, or all objects destined for the same shipping destination, etc. The human workers or automated systems are required to receive objects and to move each to their assigned collection bin. If the number of different types of input (received) objects is large, a large number of collection bins is required.
Such a system has inherent inefficiencies as well as inflexibilities since the desired goal is to match incoming objects to assigned collection bins. Such systems may require a large number of collection bins (and therefore a large amount of physical space, large capital costs, and large operating costs) in part, because sorting all objects to all destinations at once is not always most efficient.
Current state-of-the-art sortation systems rely on human labor to some extent. Most solutions rely on a worker that is performing sortation, by scanning an object from an induction area (chute, table, etc.) and placing the object in a staging location, conveyor, or collection bin. When a bin is full, another worker empties the bin into a bag, box, or other container, and sends that container on to the next processing step. Such a system has limits on throughput (i.e., how fast can human workers sort to or empty bins in this fashion) and on number of diverts (i.e., for a given bin size, only so many bins may be arranged to be within efficient reach of human workers).
Other partially automated sortation systems involve the use of recirculating conveyors and tilt trays, where the tilt trays receive objects by human sortation (human induction), and each tilt tray moves past a scanner. Each object is then scanned and moved to a pre-defined location assigned to the object. The tray then tilts to drop the object into the location. Further, partially automated systems, such as the bomb-bay style recirculating conveyor, involve having trays open doors on the bottom of each tray at the time that the tray is positioned over a predefined chute, and the object is then dropped from the tray into the chute. Again, the objects are scanned while in the tray, which assumes that any identifying code is visible to the scanner.
Such partially automated systems are lacking in key areas. As noted, these conveyors have discrete trays that can be loaded with an object; they then pass through scan tunnels that scan the object and associate it with the tray in which it is riding. When the tray passes the correct bin, a trigger mechanism causes the tray to dump the object into the bin. A drawback with such systems however, is that every divert requires an actuator, which increases the mechanical complexity and the cost per divert can be very high.
An alternative is to use human labor to increase the number of diverts, or collection bins, available in the system. This decreases system installation costs, but increases the operating costs. Multiple cells may then work in parallel, effectively multiplying throughput linearly while keeping the number of expensive automated diverts at a minimum. Such diverts do not ID an object and cannot divert it to a particular spot, but rather they work with beam breaks or other sensors to seek to ensure that indiscriminate bunches of objects get appropriately diverted. The lower cost of such diverts coupled with the low number of diverts keep the overall system divert cost low.
Unfortunately, these systems don't address the limitations to total number of system bins. The system is simply diverting an equal share of the total objects to each parallel manual cell. Thus each parallel sortation cell must have all the same collection bins designations; otherwise an object might be delivered to a cell that does not have a bin to which that object is mapped. There remains a need for a more efficient and more cost effective object sortation system that sorts objects of a variety of sizes and weights into appropriate collection bins or trays of fixed sizes, yet is efficient in handling objects of such varying sizes and weights.